Publicação
Planning and Land Use/Cover Scenarios: The Role of Probabilistic Algorithms
| Resumo: | A cellular automata (CA) model is characterized by phase transitions that can generate complex patterns through simple transition rules. As such, this technique seems suited to model the complexity of urban systems (Clarke and Gaydos, 1998; Batty, 1995). Unlike most conventional urban models that focus more or less on the spatial patterns of urban growth, cellular automata based urban models usually pay more attention to simulating the dynamic process of urban development and defining the factors or rules driving the development. By applying different transition rules, a model based on cellular automata seeks to explore how the urban system has been developing and how this system changes under certain rules or forces. The central component of a CA model is the transition rules that represent the logic of the process being modelled and, thus, determine the spatial dynamics of the system (White and Engelen, 2000). The transition rules define how changes the state of a cell in response to its current state and the states of its neighbours. This is the key component of CA because these rules represent the process of the system being modelled, and thus are essential to the success of a good modelling practice (White, 1998). For a restricted CA, the transitional rules are uniform and applied synchronously to all cells within the system. However, it has been pointed out a large number of different ways to define the transition rules. |
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| Autores principais: | Rocha, Jorge |
| Outros Autores: | Rodrigo, Catarina; Viana, Cláudia M.; Barbosa, Ângela |
| Assunto: | Probabilistic models Prediction, Land use/cover |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | capítulo de livro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | A cellular automata (CA) model is characterized by phase transitions that can generate complex patterns through simple transition rules. As such, this technique seems suited to model the complexity of urban systems (Clarke and Gaydos, 1998; Batty, 1995). Unlike most conventional urban models that focus more or less on the spatial patterns of urban growth, cellular automata based urban models usually pay more attention to simulating the dynamic process of urban development and defining the factors or rules driving the development. By applying different transition rules, a model based on cellular automata seeks to explore how the urban system has been developing and how this system changes under certain rules or forces. The central component of a CA model is the transition rules that represent the logic of the process being modelled and, thus, determine the spatial dynamics of the system (White and Engelen, 2000). The transition rules define how changes the state of a cell in response to its current state and the states of its neighbours. This is the key component of CA because these rules represent the process of the system being modelled, and thus are essential to the success of a good modelling practice (White, 1998). For a restricted CA, the transitional rules are uniform and applied synchronously to all cells within the system. However, it has been pointed out a large number of different ways to define the transition rules. |
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